SOTAVerified

Time Series Analysis

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 26–50 of 6748 papers

TitleStatusHype
United States Road Accident Prediction using Random Forest Predictor—0
On Multivariate Financial Time Series Classification—0
Time-Series Analysis on Edge-AI Hardware for Healthcare Monitoring—0
Pets: General Pattern Assisted Architecture For Time Series Analysis—0
Transformer Encoder and Multi-features Time2Vec for Financial Prediction—0
AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification—0
Foundation Models for Time Series: A Survey—0
Experimental Study on Time Series Analysis of Lower Limb Rehabilitation Exercise Data Driven by Novel Model Architecture and Large ModelsCode0
Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning—0
Did ChatGPT or Copilot use alter the style of internet news headlines? A time series regression analysis—0
Ethereum Price Prediction Employing Large Language Models for Short-term and Few-shot Forecasting—0
Deep Learning for Human Locomotion Analysis in Lower-Limb Exoskeletons: A Comparative StudyCode0
Sparseformer: a Transferable Transformer with Multi-granularity Token Sparsification for Medical Time Series Classification—0
HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction—0
Early Detection of Forest Calamities in Homogeneous Stands -- Deep Learning Applied to Bark-Beetle Outbreaks—0
Leapfrogging of a deterministic model for microeconomic systems in competitive markets—0
Multi-modal Time Series Analysis: A Tutorial and SurveyCode2
How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and OutlookCode2
Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models—0
Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Data—0
Reconstructing Noisy Gene Regulation Dynamics Using Extrinsic-Noise-Driven Neural Stochastic Differential Equations—0
A Time Series Multitask Framework Integrating a Large Language Model, Pre-Trained Time Series Model, and Knowledge Graph—0
Machine learning algorithms to predict stroke in China based on causal inference of time series analysis—0
Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop—0
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified